World Inequality Report 2022

1 Introduction

In this note, we study how to construct, or recreate charts presented in World Inequality Report. We focus on the Executive Summary, however, we hope that you can study the report in more detail using the original data provided in the site.

1.1 WIR2022

• World Inequality Report: https://wir2022.wid.world/
• Executive Summary: https://wir2022.wid.world/executive-summary/
• Methodology: https://wir2022.wid.world/methodology/

There is an R package to make it easier to download the data.

• R package to download data from the WID.world database
  • See download_wid

2 Executive Summary

2.1 Setup

• Data: https://wir2022.wid.world/www-site/uploads/2022/03/WIR2022TablesFigures-Summary.xlsx
• Data are in Excel format and charts are also created in Excel.

library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.0      ✔ purrr   1.0.0 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 ── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(readxl)
library(DT)

url_summary <- "https://wir2022.wid.world/www-site/uploads/2022/03/WIR2022TablesFigures-Summary.xlsx"
download.file(url = url_summary, destfile = "data/WIR2022TablesFigures-Summary.xlsx") 

summary_sheets <- excel_sheets("data/WIR2022TablesFigures-Summary.xlsx")
summary_sheets

 [1] "Index"     "F1"        "F2"        "F3"        "F4"        "F5."       "F6"       
 [8] "F7"        "F8"        "F9"        "F10"       "F11"       "F12"       "F13"      
[15] "F14"       "F15"       "T1"        "data-F1"   "data-F2"   "data-F3"   "data-F4"  
[22] "data-F5"   "data-F6"   "data-F7"   "data-F8"   "data-F9"   "data-F10"  "data-F11" 
[29] "data-F12"  "data-F13." "data-F14." "data-F15" 

• Note that some of the sheet names contain a period.

df_index <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "Index")
df_index

• The list of the titles of the tables and the figures.

2.2 Data and Charts

2.2.1 F1: Global income and wealth inequality, 2021

df_f1 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F1")

New names:

df_f1

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "group", values_to = "value") %>%
  ggplot(aes(x = cat, y = value, fill = group)) +
  geom_col(position = "dodge") + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  geom_text(aes(x = cat, y = value, group = group, label = scales::label_percent(accuracy=1)(value)), 
            position = position_dodge(width = 0.9)) + 
  labs(title = "Figure 1. Global income and wealth inequality, 2021",
       x = "", y = "Share of total income or wealth", fill = "")

Interpretation: The global bottom 50% captures 8.5% of total income measured at Purchasing Power Parity (PPP). The global bottom 50% owns 2% of wealth (at Purchasing Power Parity). The global top 10% owns 76% of total Household wealth and captures 52% of total income in 2021. Note that top wealth holders are not necessarily top income holders. Incomes are measured after the operation of pension and unemployment systems and before taxes and transfers.
Sources and series: wir2022.wid.world/methodology.

2.2.1.1 Explanation

2.2.1.1.1 Step 1.

• The chart does not use Top 1% data.
• There are two groups, Income and Wealth. We need to tidy the data using pivot_longer.

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "group", values_to = "value")

2.2.1.1.2 Step 2.

Now we use ggplot2 to draw a chart.

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "level", values_to = "value") %>%
  ggplot(aes(x = cat, y = value, fill = level)) +
  geom_col()

2.2.1.1.3 Step 3.

• In order to place side by side, we use an option position = dodge.

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "group", values_to = "value") %>%
  ggplot(aes(x = cat, y = value, fill = group)) +
  geom_col(position = "dodge")

2.2.1.1.4 Step 4.

• We use the percent scale on y-axis.

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "group", values_to = "value") %>%
  ggplot(aes(x = cat, y = value, fill = group)) +
  geom_col(position = "dodge") + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1))

2.2.1.1.5 Step 5.

• Add the title and the label of the y-axis, and erase the label of the x-axis and the legend.

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "group", values_to = "value") %>%
  ggplot(aes(x = cat, y = value, fill = group)) +
  geom_col(position = "dodge") + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 1. Global income and wealth inequality, 2021",
       x = "", y = "Share of total income or wealth", fill = "")

2.2.1.1.6 Step 6.

• Add the value in percent as the text to top of each column.

df_f1 %>% select(cat = ...1, 2:4) %>%
  pivot_longer(2:4, names_to = "group", values_to = "value") %>%
  ggplot(aes(x = cat, y = value, fill = group)) +
  geom_col(position = "dodge") + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  geom_text(aes(x = cat, y = value, group = group, label = scales::label_percent(accuracy=1)(value)), 
            position = position_dodge(width = 0.9)) + 
  labs(title = "Figure 1. Global income and wealth inequality, 2021",
       x = "", y = "Share of total income or wealth", fill = "")

2.2.2 F2: The poorest half lags behind: Bottom 50%, middle 40% and top 10% income shares across the world in 2021

df_f2 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F2")
df_f2

df_f2 %>% pivot_longer(3:5, names_to = "level", values_to = "value") %>%
  ggplot(aes(x = iso, y = value, fill = level)) +
  geom_col(position = "dodge") + 
  scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 2. The poorest half lags behind Bottom 50%, middle 40% \nand top 10% income shares across the world in 2021",
       x = "", y = "Share of national income (%)", fill = "")

Interpretation: In Latin America, the top 10% captures 55% of national income, compared to 36% in Europe. Income is measured after pension and unemployment contributions and benefits paid and received by individuals but before income taxes and other transfers.
Sources and series: www.wir2022.wid.world/methodology.

2.2.2.1 Explanation

2.2.2.1.1 Step 1.

• Similar to F1.

df_f2 %>% pivot_longer(3:5, names_to = "level", values_to = "value")

2.2.2.1.2 Step 2.

Since the title is very long inserted \n for line break. If \n and, then it creates a space before and.

df_f2 %>% pivot_longer(3:5, names_to = "level", values_to = "value") %>%
  ggplot(aes(x = iso, y = value, fill = level)) +
  geom_col(position = "dodge") + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 2. The poorest half lags behind Bottom 50%, middle 40% \nand top 10% income shares across the world in 2021",
       x = "", y = "Share of national income (%)", fill = "")

2.2.2.1.3 Step 3.

The labels of x-axis are overlapping. One way is to add angle. See below. Another way is to use scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) to wrap. Since label varies we use a function with a variable x. See above.

df_f2 %>% pivot_longer(3:5, names_to = "level", values_to = "value") %>%
  ggplot(aes(x = iso, y = value, fill = level)) +
  geom_col(position = "dodge") + 
  theme(axis.text.x = element_text(angle = 30, vjust = 1, hjust=1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 2. The poorest half lags behind Bottom 50%, middle 40% \nand top 10% income shares across the world in 2021",
       x = "", y = "Share of national income (%)", fill = "")

2.2.3 F3: Top 10/Bottom 50 income gaps across the world, 2021

df_f3 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F3")
df_f3

map0<-map_data("world")
map0$region[map0$region=="Democratic Republic of the Congo"]<-"DR Congo"
map0$region[map0$region=="Republic of Congo"]<-"Congo"
map0$region[map0$region=="Ivory Coast"]<-"Cote dIvoire"
map0$region[map0$region=="Vietnam"]<-"Viet Nam"
map0$region[map0$region=="Russia"]<-"Russian Federation"
map0$region[map0$region=="South Korea"]<-"Korea"
map0$region[map0$region=="UK"]<-"United Kingdom"
map0$region[map0$region=="Brunei"]<-"Brunei Darussalam"
map0$region[map0$region=="Laos"]<-"Lao PDR"
map0$region[map0$region=="Cote dIvoire"]<-"Cote d'Ivoire"
map0$region[map0$region=="Cape Verde"]<- "Cabo Verde"
map0$region[map0$region=="Syria"]<- "Syrian Arab Republic"
map0$region[map0$region=="Trinidad"]<- "Trinidad and Tobago"
map0$region[map0$region=="Tobago"]<- "Trinidad and Tobago"

df_f3 %>% 
  mutate(`Top 10 Bottom 50 Ratio` = cut(T10B50,breaks = c(5, 12, 13, 16, 19,140), include.lowest = FALSE)) %>%
  ggplot(aes(map_id = Country)) + geom_map(aes(fill = `Top 10 Bottom 50 Ratio`), map = map0) + expand_limits(x = world_map$long, y = world_map$lat) +
  labs(title = "Figure 3. Top 10/Bottom 50 income gaps across the world, 2021",
       x = "", y = "", fill = "Top 10/Bottom 50 ratio") +
  theme(legend.position="bottom", 
        axis.text.x=element_blank(), axis.ticks.x=element_blank(),
        axis.text.y=element_blank(), axis.ticks.y=element_blank()) + 
  scale_fill_brewer(palette='YlOrRd')

Interpretation: In Brazil, the bottom 50% earns 29 times less than the top 10%. The value is 7 in France. Income is measured after pension and unemployment payments and benefits received by individuals but before other taxes they pay and transfers they receive. Source and series: wir2022.wid.world/methodology.

2.2.3.1 Explanation

2.2.3.1.1 Step 1.

df_f3 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F3")
df_f3

We use world_map data. Let us look at the data first.

datatable(world_map)

Warning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlWarning: It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.html

Let us try using the standard template of ggplot. We use Country for map_id and T10B50 for numerical data. Since world_map data contains long and lat of each region, we assign them as expand_limit.

df_f3 %>% 
  ggplot(aes(map_id = Country)) + 
  geom_map(aes(fill = `T10B50`), map = world_map) +
  expand_limits(x = world_map$long, y = world_map$lat)

2.2.3.1.2 Step 2.

We observe a couple of issues by comparing with the image of the original.

F3_world_map

1. Russia, Congo and some other areas are void.
2. Scale and the legend should be improved.
3. It may be better to bring the legend at the bottom to provide more width to the world map.

The region name and the country name may be different in two datasets world_map and df_f3. Let us take care of the first issue.

In order to search names, we use DT::datatable, i.e., datatable in DT package.

datatable(df_f3)

Search for ‘russia’ and ‘congo’, we find that

• Russian Federation = Russia in world_map
• DR Congo = Democratic Republic of the Congo in world_map
• Congo = Republic of Congo in world_map

df_f3_rev <- df_f3
df_f3_rev$Country[df_f3_rev$Country == "Russian Federation"] <- "Russia"
df_f3_rev$Country[df_f3_rev$Country == "DR Congo"] <- "Democratic Republic of the Congo"
df_f3_rev$Country[df_f3_rev$Country == "Congo"] <- "Republic of Congo"

Now check the country names again using anti_join. By the code below, we can create a new table such that there is no region in world_map corresponding to Country in df_f3_rev.

df_f3_rev %>% anti_join(world_map, by = c("Country" = "region"))

We can proceed one by one. However, WIR provides the code of this part in R. So let us use it. It is in Computer Codes at the Methodology site. Download ‘Full Datasets’ and ‘Computer Codes’. Then in WIR2022 - Computer codes, find Chapter1_Maps.R.

map<-map_data("world")
map$region[map$region=="Democratic Republic of the Congo"]<-"DR Congo"
map$region[map$region=="Republic of Congo"]<-"Congo"
map$region[map$region=="Ivory Coast"]<-"Cote dIvoire"
map$region[map$region=="Vietnam"]<-"Viet Nam"
# map$region[map$region=="United Arab Emirates"]<-"UAE"

The last one for UAE seems to be wrong, so deleted.

Since the data used in the next line was not find, let me use map now.

index_region2<-read_dta("index_region.dta")
map<-left_join(map,index_region2,by=c("region"="name_region"))
map$ISO[map$region=="Greenland"]<-"GL"
map$ISO[map$region=="UAE"]<-"AE"
map$ISO[map$region=="Brunei"]<-"BR" # done
map$ISO[map$region=="Antigua"]<-"AG"
map$ISO[map$region=="Cape Verde"]<-"CV"
map$ISO[map$region=="Cote dIvoire"]<-"CI"
map$ISO[map$region=="UK"]<-"GB" # done
map$ISO[map$region=="Canary Islands"]<-"ES"
map$ISO[map$region=="French Guiana"]<-"FR"
map$ISO[map$region=="Saint Kitts"]<-"KN"
map$ISO[map$region=="South Korea"]<-"KR"
map$ISO[map$region=="Saint Martin"]<-"MF"
map$ISO[map$region=="Macedonia"]<-"MK"
map$ISO[map$region=="Russia"]<-"RU" # done
map$ISO[map$region=="Bonaire"]<-"BQ"
map$ISO[map$region=="Sint Eustatius"]<-"BQ"
map$ISO[map$region=="Saba"]<-"BQ"
map$ISO[map$region=="Laos"]<-"LA"
map$ISO[map$region=="Sint Maarten"]<-"SX"
map$ISO[map$region=="Syria"]<-"SY"
map$ISO[map$region=="Trinidad"]<-"TT"
map$ISO[map$region=="Tobago"]<-"TT"
map$ISO[map$region=="Virgin Islands"]<-"VI"
map$ISO[map$region=="Saint Vincent"]<-"VC"
map$ISO[map$region=="Grenadines"]<-"VC"
map$ISO[map$region=="French Southern and Antarctic Lands"]<-"FR"
map$ISO[map$region=="Western Sahara"]<-"WS"

map$region[map$region=="Russia"]<-"Russian Federation"
map$region[map$region=="South Korea"]<-"Korea"
map$region[map$region=="UK"]<-"United Kingdom"
map$region[map$region=="Brunei"]<-"Brunei Darussalam"
map$region[map$region=="Laos"]<-"Lao PDR"
map$region[map$region=="Cote dIvoire"]<-"Cote d'Ivoire"
map$region[map$region=="Cape Verde"]<- "Cabo Verde"
map$region[map$region=="Syria"]<- "Syrian Arab Republic"
map$region[map$region=="Trinidad"]<- "Trinidad and Tobago"
map$region[map$region=="Tobago"]<- "Trinidad and Tobago"

df_f3 %>% anti_join(map, by = c("Country" = "region"))

Is Zanzibar a part of Tanzania?

df_f3 %>% 
  ggplot(aes(map_id = Country)) + 
  geom_map(aes(fill = `T10B50`), map = map) +
  expand_limits(x = map$long, y = map$lat)

2.2.3.1.3 Step 3.

Next we add a new column Top 10 Bottom 50 Ratio by setting new breaks of T10B50. Place the legend at the bottom using theme(legend.position="bottom").

df_f3_rev %>% 
  mutate(`Top 10 Bottom 50 Ratio` = cut(T10B50, breaks = c(5, 12, 13, 16, 19, 140), include.lowest = FALSE)) %>%
  ggplot(aes(map_id = Country)) + geom_map(aes(fill = `Top 10 Bottom 50 Ratio`), map = world_map) + expand_limits(x = world_map$long, y = world_map$lat) +
  theme(legend.position="bottom")

2.2.3.1.4 Step 4.

Finally add the title, remove x and y labels, and change the legend name.

df_f3_rev %>% 
  mutate(`Top 10 Bottom 50 Ratio` = cut(T10B50, breaks = c(5, 12, 13, 16, 19, 140), include.lowest = FALSE)) %>%
  ggplot(aes(map_id = Country)) + geom_map(aes(fill = `Top 10 Bottom 50 Ratio`), map = world_map) + expand_limits(x = world_map$long, y = world_map$lat) +
  labs(title = "Figure 3. Top 10/Bottom 50 income gaps across the world, 2021",
       x = "", y = "", fill = "Top 10/Bottom 50 ratio") +
  theme(legend.position="bottom")

2.2.3.1.5 Step 5.

Remove x-axis, y-axis and ticks. If you want to change color palette, see:

• https://ggplot2.tidyverse.org/reference/scale_brewer.html
• http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/

df_f3_rev %>% 
  mutate(`Top 10 Bottom 50 Ratio` = cut(T10B50, breaks = c(5, 12, 13, 16, 19, 140), include.lowest = FALSE)) %>%
  ggplot(aes(map_id = Country)) + geom_map(aes(fill = `Top 10 Bottom 50 Ratio`), map = world_map) + expand_limits(x = world_map$long, y = world_map$lat) +
  labs(title = "Figure 3. Top 10/Bottom 50 income gaps across the world, 2021",
       x = "", y = "", fill = "Top 10/Bottom 50 ratio") +
  theme(legend.position="bottom", 
        axis.text.x=element_blank(), axis.ticks.x=element_blank(),
        axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  scale_fill_brewer(palette='YlOrRd')

2.2.4 F4: The extreme concentration of capital: wealth inequality across the world, 2021

df_f4 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F4")
df_f4

df_f4 %>% pivot_longer(3:5, names_to = "level", values_to = "value") %>%
  ggplot(aes(x = iso, y = value, fill = level)) +
  geom_col(position = "dodge") + 
  scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 4. The extreme concentration of capital: \nwealth inequality across the world, 2021",
       x = "", y = "Share of national wealth (%)", fill = "")

Interpretation: The Top 10% in Latin America captures 77% of total household wealth, versus 22% for the Middle 40% and 1% for the Bottom 50%. In Europe, the Top 10% owns 58% of total wealth, versus 38% for the Middle 40% and 4% for the Bottom 50%.
Sources and series: wir2022.wid.world/methodology.

2.2.4.1 Explanation

Almost the same as F1 and F2.

• Wrap long label: scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10))
• Per Cent: scale_y_continuous(labels = scales::percent_format(accuracy = 1))

2.2.5 F5: Global income inequality: T10/B50 ratio, 1820-2020

df_f5 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F5")
df_f5

df_f5 %>% select(year = y, ratio = t10b50) %>%
  ggplot(aes(x = year, y = ratio)) + 
  lims(y = c(10,70)) + 
  stat_smooth(span = 0.25, se = FALSE) + 
  scale_x_continuous(breaks = round(seq(1820, 2020, by = 20),1)) + 
  labs(title = "Figure 5. Global income inequality:T10/B50 ratio, 1820-2020", 
       x = "", y = stringr::str_wrap("Ratio of top 10% average income to bottom 50% average income", width = 35)) +
  annotate("text", x = 1840, y = 32, label = stringr::str_wrap("1820: average income of the global top 10% is 18x higher than average income of the bottom 50%", width = 20), size = 3) + 
  annotate("text", x = 1910, y = 49, label = stringr::str_wrap("1910: average income of the global top 10% is 41x higher than average income of the bottom 50%", width = 20), size = 3) +
    annotate("text", x = 1980, y = 60, label = stringr::str_wrap("1980: average income of the global top 10% is 53x higher than average income of the bottom 50%", width = 20), size = 3) +
    annotate("text", x = 2010, y = 32, label = stringr::str_wrap("2020: average income of the global top 10% is 38x higher than average income of the bottom 50%", width = 20), size = 3)

Interpretation. Global inequality, as measured by the ratio T10/B50 between the average income of the top 10% and the average income of the bottom 50%, more than doubled between between 1820 and 1910, from less than 20 to about 40, and stabilized around 40 between 1910 and 2020. It is too early to say whether the decline in global inequality observed since 2008 will continue. Income is measured per capita after pension and unemployement insurance transfers and before income and wealth taxes.
Sources and series: wir2022.wid.world/lmethodology and Chancel and Piketty (2021)..

2.2.5.1 Explanation

2.2.5.1.1 Step 1.

The following is enough. Here we applied str_wrap to the label of y-axis as it is very long.

df_f5 %>% select(year = y, ratio = t10b50) %>%
  ggplot(aes(x = year, y = ratio)) + 
  geom_line() +
  labs(title = "Figure 5. Global income inequality:T10/B50 ratio, 1820-2020", 
       x = "", y = stringr::str_wrap("Ratio of top 10% average income to bottom 50% average income", width = 35))

2.2.5.1.2 Step 2.

There are many way of smoothing.

Line Plot and LOESS

df_f5 %>% select(year = y, ratio = t10b50) %>%
  ggplot(aes(x = year, y = ratio)) +
  geom_line() +
  geom_smooth(method = "loess", se = FALSE) + 
  labs(title = "Figure 5. Global income inequality:", 
       subtitle = "T10/B50 ratio, 1820-2020",
       x = "", y = "Ratio of top 10% average income to bottom 50% average income")

GAM Smoothing with 24 Points

df_f5 %>% select(year = y, ratio = t10b50) %>%
  ggplot(aes(x = year, y = ratio)) +
  stat_smooth(method = "gam", formula = y ~ s(x, k = 24), se = FALSE) + 
  scale_x_continuous(breaks = round(seq(min(df_f5$y), max(df_f5$y), by = 20),1)) + 
  labs(title = "Figure 5. Global income inequality:T10/B50 ratio, 1820-2020", 
       x = "", y = stringr::str_wrap("Ratio of top 10% average income to bottom 50% average income", width = 35))

Polynomial Approximation of Degree 6

df_f5 %>% select(year = y, ratio = t10b50) %>%
  ggplot(aes(x = year, y = ratio)) +
  geom_point() +
  geom_smooth(method = "lm", formula = y ~ poly(x, 6), se = FALSE) + 
  labs(title = "Figure 5. Global income inequality:", 
       subtitle = "T10/B50 ratio, 1820-2020",
       x = "", y = stringr::str_wrap("Ratio of top 10% average income to bottom 50% average income", width = 35))

2.2.5.1.3 Step 3.

In the main chart for F5, we applied stat_smooth(span = 0.25, se = FALSE) as it is easy. You can adjust smoothness by changing the value for span.

For y-axis, following the output provided, lims(y = c(10,70)) is added and annotation.

For a long text use: stringr::str_wrap("long text", width = size) and annotate with size = fontsize.

ggforce::geom_mark_rect will add annotation in a box.

2.2.6 F6: Global income inequality: Between vs. Within country inequality (Theil index), 1820-2020

df_f6 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F6")

New names:

df_f6

df_f6 %>% select(year = "...1", 2:3) %>%
  pivot_longer(cols = 2:3, names_to = "type", values_to = "value") %>%
  mutate(types = factor(type, levels = c("Within-country inequality", "Between-country inequality"))) %>%
  ggplot(aes(x = year, y = value, fill = types)) +
  geom_area() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_x_continuous(breaks = round(seq(1820, 2020, by = 20),1)) + 
  scale_fill_manual(values=rev(scales::hue_pal()(2)), labels = function(x) str_wrap(x, width = 15)) +
  labs(title = "Figure 6. Global income inequality: \nBetween vs. within country inequality (Theil index), 1820-2020",
       x = "", y = "Share of global inequality (% of total Theil index)", fill = "") + 
  annotate("text", x = 1850, y = 0.28, label = stringr::str_wrap("1820: Between country inequality represents 11% of global inequality", width = 20), size = 3) + 
  annotate("text", x = 1980, y = 0.70, label = stringr::str_wrap("1980: Between country inequality represents 57% of global inequality", width = 20), size = 3) +
  annotate("text", x = 1990, y = 0.30, label = stringr::str_wrap("2020: Between country inequality represents 32% of global inequality", width = 20), size = 3)

Legend can be placed at the bottom: theme(legend.position="bottom")

df_f6 %>% select(year = "...1", 2:3) %>%
  pivot_longer(cols = 2:3, names_to = "type", values_to = "value") %>%
  mutate(types = factor(type, levels = c("Within-country inequality", "Between-country inequality"))) %>%
  ggplot(aes(x = year, y = value, fill = types)) +
  geom_area() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_x_continuous(breaks = round(seq(1820, 2020, by = 20),1)) + 
  scale_fill_manual(values=rev(scales::hue_pal()(2))) +
  labs(title = "Figure 6. Global income inequality: \nBetween vs. within country inequality (Theil index), 1820-2020",
       x = "", y = "Share of global inequality (% of total Theil index)", fill = "") + 
  annotate("text", x = 1850, y = 0.28, label = stringr::str_wrap("1820: Between country inequality represents 11% of global inequality", width = 20), size = 3) + 
  annotate("text", x = 1980, y = 0.70, label = stringr::str_wrap("1980: Between country inequality represents 57% of global inequality", width = 20), size = 3) +
    annotate("text", x = 1990, y = 0.30, label = stringr::str_wrap("2020: Between country inequality represents 32% of global inequality", width = 20), size = 3) +
  theme(legend.position="bottom")

Interpretation. The importance of between-country inequality in overall global inequality, as measured by the Theil index, rose between 1820 and 1980 and strongly declined since then. In 2020, between-country inequality makes-up about a third of global inequality between individuals. The rest is due to inequality within countries. Income is measured per capita after pension and unemployement insurance transfers and before income and wealth taxes.
Sources and series: wir2022.wid.world/methodology and Chancel and Piketty (2021).

2.2.6.1 Explanation

2.2.6.1.1 Step 1.

We use geom_area.

df_f6 %>% select(year = "...1", 2:3) %>%
  pivot_longer(cols = 2:3, names_to = "type", values_to = "value") %>%
  ggplot(aes(x = year, y = value, fill = type)) +
  geom_area() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 6. Global income inequality: \nBetween vs. within country inequality (Theil index), 1820-2020",
       x = "", y = "Share of global inequality (% of total Theil index)")

2.2.6.1.2 Step 2.

Since the order of the two groups are different, the following linw is added.

scale_fill_manual(values=rev(scales::hue_pal()(2)), labels = function(x) str_wrap(x, width = 15))

The second option is to control the legend to wrap.

Annotation can be omitted if we use RMarkdown to explain the charts clearly.

df_f6 %>% select(year = "...1", 2:3) %>%
  pivot_longer(cols = 2:3, names_to = "type", values_to = "value") %>%
  mutate(types = factor(type, levels = c("Within-country inequality", "Between-country inequality"))) %>%
  ggplot(aes(x = year, y = value, fill = types)) +
  geom_area() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_x_continuous(breaks = round(seq(1820, 2020, by = 20),1)) + 
  scale_fill_manual(values=rev(scales::hue_pal()(2)), labels = function(x) str_wrap(x, width = 15)) +
  labs(title = "Figure 6. Global income inequality: \nBetween vs. within country inequality (Theil index), 1820-2020",
       x = "", y = "Share of global inequality (% of total Theil index)", fill = "")

2.2.7 F7: Global income inequality, 1820-2020

df_f7 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F7")
df_f7

df_f7 %>% select(year = y, 2:4) %>%
  pivot_longer(cols = 2:4, names_to = "type", values_to = "value") %>%
  ggplot(aes(x = year, y = value, color = type)) +
  stat_smooth(span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1820, 2020, by = 20),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 7. Global income inequality, 1820-2020", 
       x = "", y = " Share of total world income (%)", color = "") +
  annotate("text", x = 1980, y = 0.20, label = stringr::str_wrap("The global bottom 50% income share remains historically low despite growth in the emerging world in the past decades.", width = 30), size = 3)

Interpretation. The share of global income going to top 10% highest incomes at the world level has fluctuated around 50-60% between 1820 and 2020 (50% in 1820, 60% in 1910, 56% in 1980, 61% in 2000, 55% in 2020), while the share going to the bottom 50% lowest incomes has generally been around or below 10% (14% in 1820, 7% in 1910, 5% in 1980, 6% in 2000, 7% in 2020). Global inequality has always been very large. It rose between 1820 and 1910 and shows little long-run trend between 1910 and 2020. Distribution of per capita incomes. Sources and series: see wir2022.wid.world/methodology and Chancel and Piketty (2021).

2.2.7.1 Explanation

2.2.7.1.1 Step 1.

Use pivot_longer to tidy the data.

df_f7 %>% select(year = y, 2:4) %>%
  pivot_longer(cols = 2:4, names_to = "type", values_to = "value") 

2.2.7.1.2 Step 2.

Use stat_smooth with span, and change the scale of x-axis and y-axis.

df_f7 %>% select(year = y, 2:4) %>%
  pivot_longer(cols = 2:4, names_to = "type", values_to = "value") %>%
  ggplot(aes(x = year, y = value, color = type)) +
  stat_smooth(span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1820, 2020, by = 20),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 7. Global income inequality, 1820-2020", 
       x = "", y = " Share of total world income (%)", color = "")

Finally, add the annotation to obtain the figure above.

2.2.8 F8: The rise of private versus the decline of public wealth in rich countries, 1970-2020

df_f8 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F8")
df_f8

df_f8 %>% 
  select(year, Germany_public = Germany, Germany_private = 'Germany (private)', 
         Spain_public = Spain, Spain_private = 'Spain (private)', 
         France_public = France, France_private = 'France (private)', 
         UK_public  = UK, UK_private = 'UK (private)', 
         Japan_public = Japan, Japan_private = 'Japan (private)', 
         Norway_public = Norway, Norway_private = 'Norway (private)',
         USA_public = USA, USA_private = 'USA (private)') %>%
  pivot_longer(!year, names_to = c("country",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = country, linetype = type), span = 0.25, se = FALSE, size=0.75) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 8. The rise of private versus the decline of public wealth in rich countries, 1970-2020", 
       x = "", y = "wealth as as % of national income", color = "", type = "")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.

Interpretation: Public wealth is the sum of all financial and non-financial assets, net of debts, held by governments. Public wealth dropped from 60% of national income in 1970 to -106% in 2020 in the UK.
Sources and series: wir2022.wid.world/methodology, Bauluz et al. (2021) and updates.

2.2.8.1 Explanation

2.2.8.1.1 Step 1.

There are two types of groups, countries and public vs private. So we change the column names first to identify that classification easily. We delete the last two columns “gwealAVGRICH” and “pwealAVGRICH” as they will not appear in the chart.

df_f8 %>% 
  select(year, Germany_public = Germany, Germany_private = 'Germany (private)', 
         Spain_public = Spain, Spain_private = 'Spain (private)', 
         France_public = France, France_private = 'France (private)', 
         UK_public  = UK, UK_private = 'UK (private)', 
         Japan_public = Japan, Japan_private = 'Japan (private)', 
         Norway_public = Norway, Norway_private = 'Norway (private)',
         USA_public = USA, USA_private = 'USA (private)') 

2.2.8.1.2 Step 2.

First separate the countries into a new column.

df_f8 %>% 
  select(year, Germany_public = Germany, Germany_private = 'Germany (private)', 
         Spain_public = Spain, Spain_private = 'Spain (private)', 
         France_public = France, France_private = 'France (private)', 
         UK_public  = UK, UK_private = 'UK (private)', 
         Japan_public = Japan, Japan_private = 'Japan (private)', 
         Norway_public = Norway, Norway_private = 'Norway (private)',
         USA_public = USA, USA_private = 'USA (private)') %>%
  pivot_longer(!year, names_to = c("country",".value"), names_sep = "_") 

2.2.8.1.3 Step 3.

Next apply pivot_longer again to form the second group.

df_f8 %>% 
  select(year, Germany_public = Germany, Germany_private = 'Germany (private)', 
         Spain_public = Spain, Spain_private = 'Spain (private)', 
         France_public = France, France_private = 'France (private)', 
         UK_public  = UK, UK_private = 'UK (private)', 
         Japan_public = Japan, Japan_private = 'Japan (private)', 
         Norway_public = Norway, Norway_private = 'Norway (private)',
         USA_public = USA, USA_private = 'USA (private)') %>%
  pivot_longer(!year, names_to = c("country",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value")

2.2.8.1.4 Step 4.

Assign colors to countries and distinguish types by linetype.

df_f8 %>% 
  select(year, Germany_public = Germany, Germany_private = 'Germany (private)', 
         Spain_public = Spain, Spain_private = 'Spain (private)', 
         France_public = France, France_private = 'France (private)', 
         UK_public  = UK, UK_private = 'UK (private)', 
         Japan_public = Japan, Japan_private = 'Japan (private)', 
         Norway_public = Norway, Norway_private = 'Norway (private)',
         USA_public = USA, USA_private = 'USA (private)') %>%
  pivot_longer(!year, names_to = c("country",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = country, linetype = type), span = 0.25, se = FALSE)

2.2.8.1.5 Step 5.

Change the line width by size, change the y-axis to percents and add the title.

df_f8 %>% 
  select(year, Germany_public = Germany, Germany_private = 'Germany (private)', 
         Spain_public = Spain, Spain_private = 'Spain (private)', 
         France_public = France, France_private = 'France (private)', 
         UK_public  = UK, UK_private = 'UK (private)', 
         Japan_public = Japan, Japan_private = 'Japan (private)', 
         Norway_public = Norway, Norway_private = 'Norway (private)',
         USA_public = USA, USA_private = 'USA (private)') %>%
  pivot_longer(!year, names_to = c("country",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = country, linetype = type), span = 0.25, se = FALSE, size=0.75) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 8. The rise of private versus the decline of public wealth in rich countries, 1970-2020", 
       x = "", y = "wealth as as % of national income", color = "", type = "")

2.2.8.1.6 Step 6.

Let us check if we use only color for each group representing columns. This is not the chart we wanted to obtain.

df_f8 %>% 
  pivot_longer(!year, names_to = "group", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group), span = 0.25, se = FALSE, size=0.75) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 8. The rise of private versus the decline of public wealth in rich countries, 1970-2020", 
       x = "", y = "wealth as as % of national income", color = "")

2.2.9 F9: Average annual wealth growth rate, 1995-2021

Original

F9_world_map

df_f9 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F9")
df_f9

df_f9 %>% select(p, wealth = 'Wealth growth 1995-2021') %>%
  ggplot() +
  stat_smooth(aes(x = p, y = wealth), span = 0.25, se = FALSE) +
#  scale_x_continuous(breaks = round(c(seq(0, 90, by = 10), 99.9, 99.99, 99.999,3))) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 9. Average annual wealth growth rate, 1995-2021", 
       x = "←1% poorest                               Global wealth group                                 0.001% richest→", y = "Per adult annual growth rate in wealth, net of inflation (%)", color = "")

Interpretation: Growth rates among the poorest half of the population were between 3% and 4% per year, between 1995 and 2021. Since this group started from very low wealth levels, its absolute levels of growth remained very low. The poorest half of the world population only captured 2.3% of overall wealth growth since 1995. The top 1% benefited from high growth rates (3% to 9% per year). This group captured 38% of total wealth growth between 1995 and 2021. Net household wealth is equal to the sum of financial assets (e.g. equity or bonds) and non-financial assets (e.g. housing or land) owned by individuals, net of their debts.
Sources and series: wir2022.wid.world/methodology.

2.2.9.1 Explanation

2.2.9.1.1 Step 1.

df_f9 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F9")
df_f9

df_f9_rev <- df_f9 %>% mutate(pp = 1/(100-p))
df_f9_rev

df_f9_rev %>% select(pp, wealth = 'Wealth growth 1995-2021') %>%
  ggplot() +
  stat_smooth(aes(x = pp, y = wealth), span = 0.25, se = FALSE) +
#  scale_x_continuous(breaks = round(c(seq(0, 90, by = 10), 99.9, 99.99, 99.999,3))) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 9. Average annual wealth growth rate, 1995-2021", 
       x = "←1% poorest                               Global wealth group                                 0.001% richest→", y = "Per adult annual growth rate in wealth, net of inflation (%)", color = "")

2.2.9.1.2 Step 2.

df_f9 %>% select(p, wealth = 'Wealth growth 1995-2021') %>%
  ggplot() +
  geom_point(aes(x = p, y = wealth)) +
#  scale_x_continuous(breaks = round(c(seq(0, 90, by = 10), 99.9, 99.99, 99.999,3))) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 9. Average annual wealth growth rate, 1995-2021", 
       x = "←1% poorest                               Global wealth group                                 0.001% richest→", y = "Per adult annual growth rate in wealth, net of inflation (%)", color = "")

Not Yet

2.2.10 F10: The share of wealth owned by the global 0.1% and billionaires, 2021

df_f10 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F10")

New names:

df_f10

df_f10 %>% 
  select(year, "Global Billionaire Wealth" = bn_hhweal, "Top 0.01%" = top0.1_hhweal) %>%
  pivot_longer(!year, names_to = "group",".value", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group), span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1995, 2020, by = 5),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_color_manual(values=rev(scales::hue_pal()(2))) +
  labs(title = "Figure 10. Extreme wealth inequality: \nthe rise of global billionaires and top 0.01%, 1995-2021", 
       x = "", y = "Share of total household wealth (%)", color = "")

Interpretation: The share of wealth detained by the world’s billionaires rose from 1% of total household wealth in 1995 to nearly 3.5% today. The threshold of top 0.01%, composed of 520 000 adults, grew from €693,000 (PPP) in 1995 to €16,666,000 today. The net household wealth is equal to the sum of financial assets (e.g. equity or bonds) and non-financial assets (e.g. housing or land) owned by individuals, net of their debts.
Sources and series: wir2022.wid.world/methodology, Bauluz et al. (2021) and updates.

2.2.10.1 Explanation

2.2.10.1.1 Step 1.

Use descriptive column names and use pivot_longer to make it tidy.

df_f10 %>% 
  select(year, "Global Billionaire Wealth" = bn_hhweal, "Top 0.01%" = top0.1_hhweal) %>%
  pivot_longer(!year, names_to = "group",".value", values_to = "value")

2.2.10.1.2 Step 2.

Use stat_smooth with span = 0.25 and change the breaks of x-labels.

df_f10 %>% 
  select(year, "Global Billionaire Wealth" = bn_hhweal, "Top 0.01%" = top0.1_hhweal) %>%
  pivot_longer(!year, names_to = "group",".value", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group), span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1995, 2020, by = 5),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 10. Extreme wealth inequality: \nthe rise of global billionaires and top 0.01%, 1995-2021", 
       x = "", y = "Share of total household wealth (%)", color = "")

2.2.10.1.3 Step 3.

Swap the colors by scale_color_manual(values=rev(scales::hue_pal()(2))). If n is the number of colors used scales::hue_pal()(n) returns the RGB color codes used. By rev, the order is reversed. See https://ggplot2.tidyverse.org/reference/scale_hue.html.

df_f10 %>% 
  select(year, "Global Billionaire Wealth" = bn_hhweal, "Top 0.01%" = top0.1_hhweal) %>%
  pivot_longer(!year, names_to = "group",".value", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group), span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1995, 2020, by = 5),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_color_manual(values=rev(scales::hue_pal()(2))) +
  labs(title = "Figure 10. Extreme wealth inequality: \nthe rise of global billionaires and top 0.01%, 1995-2021", 
       x = "", y = "Share of total household wealth (%)", color = "")

2.2.11 F11: Top 1% vs bottom 50% wealth shares in Western Europe and the US, 1910-2020

df_f11 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F11")
df_f11

df_f11 %>% 
  rename(!year, US_bot50 = USbot50, US_top1 = UStop1, EU_bot50 = EUbot50, EU_top1 = EUtop1) %>%
  pivot_longer(!year, names_to = c("group",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group, linetype = type), span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1910, 2020, by = 10),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 11. Top 1% vs bottom 50% wealth shares \n in Western Europe and the US, 1910-2020", 
       x = "", y = "Share of total personal wealth (%)", color = "", linetype = "") +
  scale_linetype_manual(values = c("dotted","solid")) +
  annotate("text", x = 2000, y = 0.50, label = stringr::str_wrap("Wealth inequality has been rising at different speeds after a historical decline. The bottom 50% has always been extremely low.", width = 30), size = 3)

Interpretation: The graph presents decennal averages of top 1% personal wealth shares in Western Europe and the US. Between 1910 and 2020, the top 1% was 55% on average in Europe vs. 43% in the US. A century later, the US is almost back to its early 20th century level. Sources and series: wir2022.wid.world/methodology.

2.2.11.1 Explanation

2.2.11.1.1 Step 1.

There are two types of groups, US vs EU and bot50 and top1. We use the same technique as in F8.

df_f11 %>% 
  rename(!year, US_bot50 = USbot50, US_top1 = UStop1, EU_bot50 = EUbot50, EU_top1 = EUtop1) %>%
  pivot_longer(!year, names_to = c("group",".value"), names_sep = "_")

2.2.11.1.2 Step 2.

Here, we try another way. Here we use two stat_smooth with different aes(). Can you see the difference of the chart above?

df_f11 %>% 
  rename(!year, US_bot50 = USbot50, US_top1 = UStop1, EU_bot50 = EUbot50, EU_top1 = EUtop1) %>%
  pivot_longer(!year, names_to = c("group",".value"), names_sep = "_") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = top1, color = group), span = 0.25, se = FALSE) +
  stat_smooth(aes(x = year, y = bot50, color = group), span = 0.25, se = FALSE, linetype = 2) +
  scale_x_continuous(breaks = round(seq(1910, 2020, by = 10),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 11. Top 1% vs bottom 50% wealth shares \n in Western Europe and the US, 1910-2020", 
       x = "", y = "Share of total personal wealth (%)", color = "") +
  annotate("text", x = 2000, y = 0.50, label = stringr::str_wrap("Wealth inequality has been rising at different speeds after a historical decline. The bottom 50% has always been extremely low.", width = 30), size = 3)

2.2.11.1.3 Step 3.

Use pivot_longer the second time.

df_f11 %>% 
  rename(!year, US_bot50 = USbot50, US_top1 = UStop1, EU_bot50 = EUbot50, EU_top1 = EUtop1) %>%
  pivot_longer(!year, names_to = c("group",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group, linetype = type), span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1910, 2020, by = 10),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 11. Top 1% vs bottom 50% wealth shares \n in Western Europe and the US, 1910-2020", 
       x = "", y = "Share of total personal wealth (%)", color = "", linetype = "") +
  annotate("text", x = 2000, y = 0.50, label = stringr::str_wrap("Wealth inequality has been rising at different speeds after a historical decline. The bottom 50% has always been extremely low.", width = 30), size = 3)

2.2.11.1.4 Step 4.

Change the order of linetypes manually.

df_f11 %>% 
  rename(!year, US_bot50 = USbot50, US_top1 = UStop1, EU_bot50 = EUbot50, EU_top1 = EUtop1) %>%
  pivot_longer(!year, names_to = c("group",".value"), names_sep = "_") %>%
  pivot_longer(3:4, names_to = "type", values_to = "value") %>%
  ggplot() +
  stat_smooth(aes(x = year, y = value, color = group, linetype = type), span = 0.25, se = FALSE) +
  scale_x_continuous(breaks = round(seq(1910, 2020, by = 10),1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 11. Top 1% vs bottom 50% wealth shares \n in Western Europe and the US, 1910-2020", 
       x = "", y = "Share of total personal wealth (%)", color = "", linetype = "") +
  scale_linetype_manual(values = c("dotted","solid")) +
  annotate("text", x = 2000, y = 0.50, label = stringr::str_wrap("Wealth inequality has been rising at different speeds after a historical decline. The bottom 50% has always been extremely low.", width = 30), size = 3)

2.2.12 F12: Female share in global labor incomes, 1990-2020

df_f12 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F12")

New names:

df_f12

df_f12 %>% 
  select(year = "Data needs to be updated", value = ...2) %>%
  filter(!is.na(year)) %>%
  ggplot(aes(x = year, y = value)) +
  geom_col(width = 0.5, fill = scales::hue_pal()(2)[2]) +
  geom_hline(yintercept = 0.5, linetype = 2, colour = scales::hue_pal()(2)[1]) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 12. Female share in global labor incomes, 1990-2020", 
        x = "", y = "") +
  annotate("text", x = 1, y = 0.48, label = "Gender parity", size = 3) +
  annotate("text", x = 5.2, y = 0.47, label = stringr::str_wrap("Women make only 35% of global labor incomes, men make the remaining  65%.", width = 40), size = 3)

Interpretation: The share of female incomes in global labour incomes was 31% in 1990 and nears 35% in 2015-2020. Today, males make up 65% of total labor incomes. Sources and series: wir2022.wid.world/methodology and Neef and Robilliard (2021).

2.2.12.1 Explanation

2.2.12.1.1 Step 1.

Change the column names and delete the rows with NAs.

df_f12 %>% 
  select(year = "Data needs to be updated", value = ...2) %>%
  filter(!is.na(year))

2.2.12.1.2 Step 2.

Add geom_col and geom_hline with colors scales::hue_pal()(2)[2] and scales::hue_pal()(2)[1].

df_f12 %>% 
  select(year = "Data needs to be updated", value = ...2) %>%
  filter(!is.na(year)) %>%
  ggplot(aes(x = year, y = value)) +
  geom_col(width = 0.5, fill = scales::hue_pal()(2)[2]) +
  geom_hline(yintercept = 0.5, linetype = 2, colour = scales::hue_pal()(2)[1]) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 12. Female share in global labor incomes, 1990-2020", 
        x = "", y = "") +
  annotate("text", x = 1, y = 0.48, label = "Gender parity", size = 3) +
  annotate("text", x = 5.2, y = 0.47, label = stringr::str_wrap("Women make only 35% of global labor incomes, men make the remaining  65%.", width = 40), size = 3)

2.2.13 F13: Female labor income share across the world, 1990-2020

Note that the sheet name of F13 has period at the end. Note that summary_sheets[30] =data-F13. with a period.

df_f13 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F13.")

New names:

df_f13

df_f13 %>% pivot_longer(2:9, names_to = "region", values_to = "value") %>%
  ggplot(aes(x = region, y = value, fill = ...1)) +
  geom_col(position = "dodge") + 
  geom_hline(yintercept = 0.5, linetype = 2, colour = "red") +
  scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 12)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 13. Female labor income share across the world, 1990-2020", 
        x = "", y = "", fill = "") +
  annotate("text", x = 1.2, y = 0.48, label = "Gender parity", size = 3)

Interpretation: The female labour income share rose from 34% to 38% in North America between 1990 and 2020. Sources and series: wir2022.wid.world/methodology and Neef and Robilliard (2021).

2.2.13.1 Explanation

Not so difficult.

2.2.14 F14: Global carbon inequality, 2019. Group contribution to world emissions (%)

Note that the sheet name of F14 has period at the end. Note that summary_sheets[31] =data-F14. with a period.

df_f14 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F14.")
df_f14

• \n for line break in the title.

df_f14 %>% 
  ggplot(aes(x = Group, y = Share)) +
  geom_col(width = 0.5, fill = scales::hue_pal()(1)[1]) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(title = "Figure 14. Global carbon inequality, \n2019 Group contribution to world emissions (%)", 
       x = "", y = "Share of world emissions (%)")

Interpretation: Personal carbon footprints include emissions from domestic consumption, public and private investments as well as imports and exports of carbon embedded in goods and services traded with the rest of the world. Modeled estimates based on the systematic combination of tax data, household surveys and input-output tables. Emissions split equally within households. Sources and series: wir2022.wid.world/methodology and Chancel (2021).

2.2.14.1 Explanation

Not so difficult. You can assign color name. See http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/.

2.2.15 F15: Per capita emissions acriss the world, 2019

df_f15 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", sheet = "data-F15")
df_f15

df_f15 %>% mutate(region = rep(regionWID[!is.na(regionWID)], each = 3)) %>%
  select(region, group, tcap) %>%
  ggplot(aes(x = region, y = tcap, fill = group)) +
  geom_col(position = "dodge") + 
  scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  labs(title = "Figure 15 Per capita emissions across the world, 2019", 
       x = "", y = "tonnes of CO2e per person per year", fill = "")

Interpretation: Personal carbon footprints include emissions from domestic consumption, public and private investments as well as imports and exports of carbon embedded in goods and services traded with the rest of the world. Modeled estimates based on the systematic combination of tax data, household surveys and input-output tables. Emissions split equally within households. Sources and series: wir2022.wid.world/methodology and Chancel (2021).

2.2.15.1 Explanation

2.2.15.1.1 Step 1.

It is in Excel stype and there are missing values in the first column. In order to add a new column, let us check the following.

region_test <- rep(df_f15$regionWID[!is.na(df_f15$regionWID)], each = 3)
region_test

 [1] "East Asia"               "East Asia"               "East Asia"              
 [4] "Europe"                  "Europe"                  "Europe"                 
 [7] "North America"           "North America"           "North America"          
[10] "South & South-East Asia" "South & South-East Asia" "South & South-East Asia"
[13] "Russia & Central Asia"   "Russia & Central Asia"   "Russia & Central Asia"  
[16] "MENA"                    "MENA"                    "MENA"                   
[19] "Latin America"           "Latin America"           "Latin America"          
[22] "Sub-Saharan Africa"      "Sub-Saharan Africa"      "Sub-Saharan Africa"     

2.2.15.1.2 Step 2.

Add the names of the region in the last column by mutate and choose columns by select.

df_f15 %>% mutate(region = rep(regionWID[!is.na(regionWID)], each = 3))

df_f15 %>% mutate(region = rep(regionWID[!is.na(regionWID)], each = 3)) %>%
  select(region, group, tcap)

2.2.15.1.3 Step 3.

Now it is not difficult to draw a chart.

df_f15 %>% mutate(region = rep(regionWID[!is.na(regionWID)], each = 3)) %>%
  select(region, group, tcap) %>%
  ggplot(aes(x = region, y = tcap, fill = group)) +
  geom_col(position = "dodge") + 
  scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 10)) +
  labs(title = "Figure 15 Per capita emissions across the world, 2019", 
       x = "", y = "tonnes of CO2e per person per year", fill = "")

2.2.16 T1 Global millionaires and billionaires, 2021

Check the format of the sheet and add skip=4, n_max=7.

df_t1 <- read_excel("data/WIR2022TablesFigures-Summary.xlsx", 
                    sheet = "T1", skip = 4, n_max = 7)
df_t1

Interpretation: In 2021, there were 62.2 million people in the world owning more than $1 million (measured at Market Exchange Rates). Their average wealth was $ 2.8 million, representing a total of $174 trillion. In our Tax scenario 2, a global progressive wealth tax would yield 2.1% of global income, taking into account capital depreciation and evasion. Sources and series: wir2022.wid.world/methodology.

3 Package: WID

WID is a package in development stage, and it is not available at CRAN. So we need a spacial way to install the package.

To install, run the following code in Console. If you are recommended to update, select 1 by choosing ‘All’.

For references use ‘?download_wid’ or put ‘download_wid’ in the search box under Help.

It is similar to WDI. For more detail and examples, see vignettes.

3.1 Examples

library("wid")

3.1.1 Evolution of national income over long period

We now plot the evolution of average net national income per adult in Japan, France, Germany, the United Kingdom and the United States.

# Average national income data
data <- download_wid(
    indicators = "anninc", # Average net national income
    areas = c("JP", "FR", "US", "DE", "GB"),
    ages = 992 # Adults
) %>% rename(value_lcu = value)
# Purchasing power parities with US dollar
ppp <- download_wid(
    indicators = "xlcusp", # US PPP
    areas = c("JP", "FR", "US", "DE", "GB"), # France, China and United States
    year = 2016 # Reference year only
) %>% rename(ppp = value) %>% select(-year, -percentile)
# Convert from local currency to PPP US dollar
data <- merge(data, ppp, by = "country") %>%
    mutate(value_ppp = value_lcu/ppp) %>%
    filter(year %in% 1950:2021)
ggplot(data) +
    geom_line(aes(x = year, y = value_ppp, color = country, linetype=country)) +
    scale_y_log10(breaks = c(2e3, 5e3, 1e4, 2e4, 5e4)) +
    ylab("2016 $ PPP") +
    scale_color_discrete(
        labels = c("JP" = "Japan", "US" = "USA", "FR" = "France", "DE" = "Germany", "GB" = "UK")
    ) + 
    scale_linetype_discrete(
        labels = c("JP" = "Japan", "US" = "USA", "FR" = "France", "DE" = "Germany", "GB" = "UK")
    ) + 
    ggtitle("Average net national income per adult")